Label Distribution Learning from Logical Label
Authors: Yuheng Jia, Jiawei Tang, Jiahao Jiang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets prove that the proposed approach can construct a reliable LDL model directly from the logical label, and produce more accurate label distribution than the state-of-the-art LE methods. The code and the supplementary file can be found in https://github.com/seutjw/DLDL. |
| Researcher Affiliation | Academia | Yuheng Jia1,2 , Jiawei Tang1,2 , Jiahao Jiang1,2 1School of Computer Science and Engineering, Southeast University 2Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China |
| Pseudocode | Yes | Algorithm 1 Solve the Problem in (11); Algorithm 2 The DLDL Algorithm |
| Open Source Code | Yes | The code and the supplementary file can be found in https://github.com/seutjw/DLDL. |
| Open Datasets | Yes | We select six real-world datasets from various fields for experiment. Natural Scene (abbr. NS) [Geng, 2016; Geng et al., 2022] is generated from the preference distribution of each scene image, SCUT-FBP (abbr. SCUT) [Xie et al., 2015] is a benchmark dataset for facial beauty perception, RAF-ML (abbr. RAF) [Shang and Deng, 2019] is a multi-label facial expression dataset, SCUT-FBP5500 (abbr. FBP) [Liang et al., 2018] is a big dataset for facial beauty prediction, Ren CECps (abbr. REN) [Quan and Ren, 2009] is a Chinese emotion corpus of weblog articles, and Twitter LDL (abbr. Twitter) [Yang et al., 2017] is a visual sentiment dataset. |
| Dataset Splits | Yes | In this paper, we split each dataset into three subsets: training set (60%), validation set (20%) and testing set (20%). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In the recovery experiment, for DLDL, α and γ are chosen among {10 3, 10 2, , 10, 102}, β is selected from {10 3, 10 2, , 1, 10}, the maximum of iterations t is fixed to 5, the number of neighbors k is set to 20. |